A successful Salesforce Data Cloud implementation requires some key considerations and upfront planning. This guide walks through the essential steps, from defining goals and building a data structure to implementing governance, security, and testing protocols.
Introducing new technology to your business is never as easy as flipping a switch. A Salesforce Data Cloud implementation is no different. While Salesforce Data Cloud is a powerful tool that can transform how you use your business data to make strategic decisions, there are still numerous best practices and key considerations to keep in mind when rolling it out to your users.
In this Salesforce Data Cloud implementation guide, we’ll provide steps to help your employees enjoy these and other benefits of your Salesforce Data Cloud transformation.
Here’s what we’ll cover:
What is Salesforce Data Cloud?
Every Salesforce Data Cloud Implementation Needs a Plan
Additional Considerations for Your Salesforce Data Cloud Implementation
Overcoming Salesforce Data Cloud Integration Challenges
What is Salesforce Data Cloud?
Data is a critical asset for modern businesses. It drives informed decision-making, shapes strategic initiatives, helps organizations respond quickly to market changes, and delivers customer insights that enable personalized experiences. As a result, companies that effectively use data gain a competitive advantage, become more innovative, and set themselves up for growth.
The challenge? We generate so much data today, and it comes at us from many sources. To get the most value from all this data, we need a tool that can pull all this data together and make it usable.
Salesforce Data Cloud is such a tool. Using methods called ingestion and federation, Data Cloud connects to data stored in non-Salesforce sources (like ERP and data warehouses) and unstructured customer data from sources like Google reviews, website behavior, chat messages, and more.
One differentiating feature of Data Cloud is zero-copy integration. Zero-copy integration allows data from external databases to be available as objects within the Data Cloud data lake without the need to move, copy or reformat any of the data.
Instead, Data Cloud uses connections to external sources to create data-lake objects (DLOs). DLOs simply point to the data. As part of the harmonization and unification, Data Cloud uses the data in those DLOs along with your Salesforce Sales Cloud, Service Cloud, and Marketing Cloud data to create a 360-degree view of your customer. The unified data is then available as part of the Einstein 1 Platform, enabling insights, analytics, automation, and AI capabilities.
Though complex at a glance, this method is a more efficient way of capturing data, and it enables such benefits as:
- Real-time data processing and analytics
- Cross-cloud data unification
- Customer 360 view capabilities
- AI and ML capabilities
- Predictive analytics for customer behavior and sales forecasting
- Enhanced personalization through automated segmentation
- A set of best practices for AI model training and deployment
However, the tool represents a lot of change, too. Let’s dive into how to best introduce that change.
Every Salesforce Data Cloud Implementation Needs a Plan
While Salesforce Data Cloud is a flexible platform, investing in a solid plan upfront will pay off throughout the implementation.
Know Your Goals
Start by defining your organizational goals. Understanding your specific needs, goals and business challenges will help you assess and prioritize your data in a way that supports your most pressing use cases. You can then use this information to create an implementation roadmap to guide your data architecture design and data model.
Plan a Data Structure to Meet Your Goals
Your data is only as useful as your employees’ ability to access and work with it. Define data entities, relationships, and hierarchies that align with your business processes. For a Salesforce Data Cloud implementation, you’ll also want to determine the custom fields and objects that will help you extend the Salesforce data model to provide your 360-degree customer view.
Build in Governance and Security
Any software implementation also requires good governance, so you will need to define data formats (like dates, timestamps and phone numbers), naming conventions, and other rules. Governance also supports cybersecurity and compliance, two essential requirements. Now is the time to determine user access controls, data protection measures, and compliance with relevant regulations, like GDPR or CCPA.
Go Deeper on Data
With these foundations in place, you can start thinking more deeply about preparing your data for your Salesforce Data Cloud implementation. Identify how source system DLOs map into the Salesforce Data Cloud data model and identify the required data translations. Matching and resolution rules will help to address data conflicts, and you should identify the neural network model to consume your unstructured data and define chunking approaches and vector embedding requirements. This work will help harmonize and unify your data.
Test, Test, and Test Some More
Testing is also integral to any software implementation. Your testing plan should include both data preparation and data consumption:
Data Preparation
- Data Security: user access and permissions
- Data Ingestion: Salesforce’s method of using a data stream to bring data from an external source into Data Cloud while retaining the source’s fields and data types
- Data Mapping: harmonization of data
- Identity Resolution: unification of data
Data Consumption
- Segmentation: breaking your data into useful segments to understand, target and analyze your customers
- Activation: the process of publishing a segment to a specific location (the activation target)
- Insights and Analysis: features that let you define and calculate 360-degree customer views using your entire Salesforce Data Cloud digital state
Embrace the Change
Implementing Salesforce Data Cloud will change the way people work. Build communication and training plans for end-users and administrators to ensure that everyone knows how to use the system and follows best practices. Your deployment strategy should include timelines, milestones and contingency plans covering all aspects of the installation, configuration and data migration.
Additional Considerations for Your Salesforce Data Cloud Implementation
Once you start exploring the many ways Salesforce Data Cloud will benefit your organization, it’s easy to think you should use every piece of data about your customers from every data source you have. However, you must be thoughtful and intentional.
The work you’ve already done to identify your goals, build a data structure, and prepare your data provides a great foundation for identifying the best data for your needs, but the effort it will take effort to ingest, map, harmonize, and unify information from multiple sources will help further narrow your scope. In addition, Salesforce Data Cloud’s pricing model should also help you think more carefully about how much data you need.
Master Salesforce Data Cloud’s Pricing Model
Unlike the license pricing of other Salesforce products, Data Cloud pricing is consumption-based, meaning you pay for the services you use.
For example, you will pay consumption costs for using Data Services Credits and Segmentation & Activation Credits. As a result, ingesting unnecessary data records and elements in Data Cloud can increase your costs. Segmentation and activation actions, such as publishing segments more frequently than needed, will also add to your costs.
To limit the cost of Salesforce Data Cloud, you must optimize your data usage. Start by only using the data elements needed to support a small number of use cases, and limit the volume of test data to the smallest set that supports your testing needs. Adding data elements later is a straightforward process, so you can grow Salesforce Data Cloud incrementally as you learn more about its capabilities and their benefits.
Explore the Salesforce Data Cloud Sandbox
Data Cloud can also be used in a sandbox. However, sandbox activities also count against your credit allocation, so you should account for the volume of data you consume and the frequency of segment publication in your sandbox environment. The benefit of the sandbox is that it allows you to experiment with Salesforce Data Cloud safely while performing all the activities of a live implementation, from data modeling, harmonization, and unification to segmentation, activation, and insights.
Use Data Cloud with Salesforce’s Einstein 1 Platform
The Salesforce Einstein 1 Platform uses the power of generative AI to unlock the full potential of Salesforce Data Cloud. Here are some ways you can improve decision-making and enhance customer experiences with the Einstein 1 Platform:
- Predictive Analytics: Use AI algorithms to analyze historical data stored in Salesforce Data Cloud and predict future trends, customer behaviors, and sales outcomes to inform more data-driven decisions.
- Automated Insights: Generate insights automatically from your data by surfacing trends and anomalies without manual intervention.
- Personalization: Implement AI-driven personalization strategies to tailor marketing campaigns and customer interactions based on data insights, leading to increased customer engagement.
- Sales Forecasting: Enhance sales forecasting accuracy by analyzing data trends and customer behaviors, which can help sales teams allocate resources more effectively.
- Segmentation and Targeting: Analyze customer data and create precise customer segments for targeted marketing efforts. Segmentation and targeting provide more relevant messaging and improve conversion rates.
- Natural Language Processing (NLP): Use NLP to analyze customer feedback, social media interactions, support tickets and more to identify customer sentiment and improvement areas.
Note that you will need to do some additional data preparation to use Einstein. The Salesforce CRM Analytics tool can help you load and transform data from one or more data sources into a dataset for Einstein Discovery, the tool within the Einstein 1 Platform suite that can quickly operationalize data analysis, predictions and improvements.
Overcoming Salesforce Data Cloud Integration Challenges
After completing these critical planning activities, you’re ready to implement Salesforce Data Cloud. As you begin your implementation journey, you’ll likely encounter integration challenges, but you can successfully address these potential hiccups.
Data Challenges
Several of the challenges pertain to data. Fortunately, you have likely already begun to address one major hurdle through your data preparation for Salesforce Data Cloud: data quality and inconsistency. Data must be clean, consistent, and up-to-date for both Salesforce Data Cloud and the Einstein 1 Platform.
To achieve this goal, once you have completed your prework of aligning your data needs and business goals, develop processes for cleaning and transforming data to resolve inconsistencies, duplicates, and errors. When in doubt, remember the acronym ROT — relevant, outdated and trivial. Data that is irrelevant to your business goals, outdated, or simply unnecessary will slow your results and drive your costs up. When your data is ready, you map it from the source systems to the Salesforce data model.
Data silos present an even more challenging problem because they are about people and systems as well as data. Different departments in your organization may have been working for years under a model of “my data” vs. “your data.” But for systems like Salesforce Data Cloud or Einstein 1 Platform to work, you must address those models, paying careful attention to change management and communicating your data strategy to all employees.
Other data challenges fall into a more technical category. Aligning data structures from various sources with Salesforce Data Cloud can lead to complex data mapping. Solving this problem begins with the data model you have already begun to create. Be mindful of all the different data formats, types and relationships, but always go back to your North Star of creating a data model and strategy that incorporates your particular business and goals.
Similarly, data harmonization and unification require that you outline steps to reconcile and merge data from disparate sources, ensuring that data is accurate and consistent across the organization. You must also carefully think through matching and identity resolution rules but avoid creating rules that are too strict. Instead, allow for stages of matching and resolution, with checks after each change.
API Limitations
Integration often relies on APIs, which may have rate limits, size limits, versioning issues, or may not support the needed data operations effectively. Implementing Salesforce Data Cloud provides many predefined connectors to systems of all types, and the company regularly adds more. Use these connectors, but keep in mind your Salesforce Data Cloud ingestion and publication limitations.
Security and Compliance
Security and compliance are very important given the sensitivity and diversity of customer data your Salesforce Data Cloud will manage. Everyone on your team must understand the principles of data ethics, privacy and consent. All employees should also be trained on security best practices for the cloud, AI and the workplace. Finally, provide regular training and education about any regulations that apply to your industry, like GDPR or HIPAA.
Get the Most From Your Salesforce Data Cloud Implementation With Segmentation, Activation and Insights
In addition to the benefits discussed above, Salesforce Data Cloud’s insights, segmentation and activation capabilities present another set of powerful advantages. These tools help deliver that 360-degree customer view. Segmentation simply organizes your data into “segments” that make it useful for analysis, while activation converts segments to activation targets and publishes them to platforms you can interact with. This allows you to more easily gather insights such as:
- Calculated insights at the profile, segment and population levels
- Streaming insights based on real-time customer engagement data over a rolling time window
- Real-time insights to see if the user’s activity meets defined thresholds
Salesforce Data Cloud also includes built-in privacy and security features to enhance your regulatory compliance strategies, your policies for ethically collecting and using data, and your governance frameworks. And, because Data Cloud is steeped in data and metrics, you’ll find it easier to identify Implementation KPIs, create iterative optimization strategies, and discover user adoption and training best practices.
These metrics for measuring success are important because your Salesforce Data Cloud implementation will not be a one-time effort. The more you use Data Cloud, the more you will learn and the more you can adjust it to improve performance. As your employees understand the tool and its benefits, encourage them to become your best Salesforce Data Cloud ambassadors.
Conclusion
Your Salesforce Data Cloud implementation requires you to balance technical requirements and organizational needs. You must think strategically about your data goals, sources, structure, and model to ensure they align with your organization’s goals.
However, the hard work will pay off because of the unified customer data, real-time analytics, and personalized experiences that Salesforce Data Cloud can deliver. With regular training and a continuous improvement mindset, Salesforce Data Cloud can be your portal to the enhanced customer experiences that drive business success today.
Trying to figure out which aspects of Salesforce Data Cloud will best support your business can be overwhelming. Our team is here to help you set up exactly what you need to ensure success. Contact us
